Improving CT prediction of treatment response in patients with metastatic colorectal carcinoma using statistical learning theory

Abstract Background Significant interest exists in establishing radiologic imaging as a valid biomarker for assessing the response of cancer to a variety of treatments. To address this problem, we have chosen to study patients with metastatic colorectal carcinoma to learn whether statistical learning theory can improve the performance of radiologists using CT in predicting patient treatment response to therapy compared with the more traditional RECIST (Response Evaluation Criteria in Solid Tumors) standard. Results Predictions of survival after 8 months in 38 patients with metastatic colorectal carcinoma using the Support Vector Machine (SVM) technique improved 30% when using additional information compared to WHO (World Health Organization) or RECIST measurements alone. With both Logistic Regression (LR) and SVM, there was no significant difference in performance between WHO and RECIST. The SVM and LR techniques also demonstrated that one radiologist consistently outperformed another. Conclusions This preliminary research study has demonstrated that SLT algorithms, properly used in a clinical setting, have the potential to address questions and criticisms associated with both RECIST and WHO scoring methods. We also propose that tumor heterogeneity, shape, etc. obtained from CT and/or MRI scans be added to the SLT feature vector for processing

Opportunities in cancer imaging: a review of oesophageal, gastric and colorectal malignancies

The incidence of gastrointestinal (GI) malignancy is increasing worldwide. In particular, there is a concerning rise in incidence of GI cancer in younger adults. Direct endoscopic visualisation of luminal tumour sites requires invasive procedures, which are associated with certain risks, but remain necessary because of limitations in current imaging techniques and the continuing need to obtain tissue for diagnosis and genetic analysis; however, management of GI cancer is increasingly reliant on non-invasive, radiological imaging to diagnose, stage, and treat these malignancies. Oesophageal, gastric, and colorectal malignancies require specialist investigation and treatment due to the complex nature of the anatomy, biology, and subsequent treatment strategies. As cancer imaging techniques develop, many opportunities to improve tumour detection, diagnostic accuracy and treatment monitoring present themselves. This review article aims to report current imaging practice, advances in various radiological modalities in relation to GI luminal tumour sites and describes opportunities for GI radiologists to improve patient outcomes

EPMA position paper in cancer:current overview and future perspectives

At present, a radical shift in cancer treatment is occurring in terms of predictive, preventive, and personalized medicine (PPPM). Individual patients will participate in more aspects of their healthcare. During the development of PPPM, many rapid, specific, and sensitive new methods for earlier detection of cancer will result in more efficient management of the patient and hence a better quality of life. Coordination of the various activities among different healthcare professionals in primary, secondary, and tertiary care requires well-defined competencies, implementation of training and educational programs, sharing of data, and harmonized guidelines. In this position paper, the current knowledge to understand cancer predisposition and risk factors, the cellular biology of cancer, predictive markers and treatment outcome, the improvement in technologies in screening and diagnosis, and provision of better drug development solutions are discussed in the context of a better implementation of personalized medicine. Recognition of the major risk factors for cancer initiation is the key for preventive strategies (EPMA J. 4(1):6, 2013). Of interest, cancer predisposing syndromes in particular the monogenic subtypes that lead to cancer progression are well defined and one should focus on implementation strategies to identify individuals at risk to allow preventive measures and early screening/diagnosis. Implementation of such measures is disturbed by improper use of the data, with breach of data protection as one of the risks to be heavily controlled. Population screening requires in depth cost-benefit analysis to justify healthcare costs, and the parameters screened should provide information that allow an actionable and deliverable solution, for better healthcare provision

Identifying Quantitative Enhancement-Based Imaging Biomarkers In Patients With Colorectal Cancer Liver Metastases Undergoing Loco-Regional Tumor Therapy

The purpose of this study was to test and compare the ability of radiologic measurements of lesion diameter, volume, and enhancement on baseline magnetic resonance (MR) images to be predictors of overall survival (OS) and markers of treatment response in patients with liver-dominant colorectal cancer metastases undergoing loco-regional tumor therapies. This retrospective study included 88 patients with colorectal cancer (CRC) liver metastases, treated with transarterial chemoembolization (TACE) or Y90 transarterial radioembolization (TARE) between 2001 and 2014. All patients received contrast-enhanced MRI prior to therapy. Semi-automated whole liver and tumor segmentations of three dominant lesions were performed on baseline MRI to calculate total tumor volume (TTV) and total liver volumes (TLV). Quantitative 3D analysis was performed to calculate enhancing tumor volume (ETV), enhancing tumor burden (ETB, calculated as ETV/TLV), enhancing liver volume (ELV), and enhancing liver burden (ELB, calculated as ELV/TLV). Overall and enhancing tumor diameters were also measured. Response assessment was analyzed in a subset of 63 patients who received 1-month MRI follow-up imaging using RECIST, mRECIST, change in ELV (deltaELV), vRECIST and qEASL. A modified Kaplan-Meier method was used to determine appropriate cutoff values to stratify patients based on these metrics. The predictive value of each parameter was assessed by Kaplan-Meier survival curves as well as univariate and multivariate cox proportional hazard models (statistical significance defined as p \u3c .05). In baseline imaging analysis, all methods except ELB achieved statistically significant separation of survival curves. Multivariate analysis showed a HR of 2.1 (95% CI 1.3-3.4, p=0.004) for enhancing tumor diameter, HR 1.7 (95% CI 1.1-2.8, p=0.04) for TTV, HR 2.3 (95% CI 1.4-3.9, p In conclusion, tumor enhancement of CRC liver metastases on baseline MR imaging is strongly associated with patient survival after loco-regional tumor therapy, suggesting that ETV and ETB are better prognostic indicators than non-enhancement based and one-dimensional based markers. However, while volumetric-based methods are superior to 1D methods, enhancement-based methods of treatment response assessment were not successful in predicting survival. A potential implication of these findings as novel staging markers warrants prospective validation

Patterns of injury and violence in Yaoundé Cameroon: an analysis of hospital data.

BackgroundInjuries are quickly becoming a leading cause of death globally, disproportionately affecting sub-Saharan Africa, where reports on the epidemiology of injuries are extremely limited. Reports on the patterns and frequency of injuries are available from Cameroon are also scarce. This study explores the patterns of trauma seen at the emergency ward of the busiest trauma center in Cameroon's capital city.Materials and methodsAdministrative records from January 1, 2007, through December 31, 2007, were retrospectively reviewed; information on age, gender, mechanism of injury, and outcome was abstracted for all trauma patients presenting to the emergency ward. Univariate analysis was performed to assess patterns of injuries in terms of mechanism, date, age, and gender. Bivariate analysis was used to explore potential relationships between demographic variables and mechanism of injury.ResultsA total of 6,234 injured people were seen at the Central Hospital of Yaoundé's emergency ward during the year 2007. Males comprised 71% of those injured, and the mean age of injured patients was 29 years (SD = 14.9). Nearly 60% of the injuries were due to road traffic accidents, 46% of which involved a pedestrian. Intentional injuries were the second most common mechanism of injury (22.5%), 55% of which involved unarmed assault. Patients injured in falls were more likely to be admitted to the hospital (p < 0.001), whereas patients suffering intentional injuries and bites were less likely to be hospitalized (p < 0.001). Males were significantly more likely to be admitted than females (p < 0.001)DiscussionPatterns in terms of age, gender, and mechanism of injury are similar to reports from other countries from the same geographic region, but the magnitude of cases reported is high for a single institution in an African city the size of Yaoundé. As the burden of disease is predicted to increase dramatically in sub-Saharan Africa, immediate efforts in prevention and treatment in Cameroon are strongly warranted

Quantitative Imaging Biomarkers for Yttrium-90 Distribution and Tumor Response after Transarterial Radioembolization in malignant Liver Tumors

Zielsetzung: Die Identifizierung prätherapeutischer bildbasierter Tumorcharakteristika zur Vorhersage der Yttrium-90 (90Y) Verteilung in der posttherapeutischen Bremsstrahlung single photon emission computed tomography (SPECT) und des Tumoransprechens in Patienten mit primären und sekundären Lebertumoren nach selektiver interner Radiotherapie (SIRT). Methoden: In diese retrospektive Studie wurden 38 Patienten mit Lebertumoren, die mit Harzmikrosphären-basierter SIRT behandelt wurden, eingeschlossen. Die Patientenkohorte bestand aus 23 Patienten mit Hepatozellulärem Karzinom (HCC) und 15 Patienten mit anderen malignen Lebertumoren (non-HCC). Die Bildgebung umfasste eine multiphasische Kontrastmittel-MRT oder CT vor SIRT und eine Bremsstrahlung SPECT unmittelbar nach SIRT. Das totale und kontrastmittelaufnehmende Tumorvolumen (ETV [cm3] and %), und die totale und kontrastmittelaufnehmende Tumorlast (%) wurden volumetrisch auf der prätherapeutischen Bildgebung quantifiziert. Bis zu zwei dominante Tumore pro behandeltem Leberlappen wurden analysiert. Nach der multimodalen Bildregistrierung von prätherapeutischer MRT oder CT auf die SPECT/CT, wurde die 90Y Verteilung in der SPECT als Tumor-zu-normale-Leber Verhältnis (TNR) volumetrisch bestimmt. Das Tumoransprechen wurde anhand der quantitative European Association for the Study of the Liver (qEASL) und response evaluation criteria in solid tumors 1.1 (RECIST1.1) Kriterien beurteilt. Klinische Parameter, wie z.B. Child-Pugh Stadien, wurden ebenfalls untersucht. Statistische Tests umfassten den nicht-parametrischen Mann-Whitney U, die bivariate Pearson Korrelation und Lineare Regression. Ergebnisse: In HCC korrelierte ein höheres prätherapeutisches ETV% mit höherer TNR in der SPECT, und damit mehr 90Y Aufnahme des Tumors relativ zum umliegenden Lebergewebe (P<0.001). In non-HCC bestand die Korrelation zwischen höherer ETV% und TNR auch (P=0.039). Zusätzlich zeigten HCC Patienten mit Child-Pugh B signifikant mehr 90Y Ablagerung in nicht-tumoröser Leber, gemessen als niedrigere TNR, als Child-Pugh A Patienten (P=0.021). Die Nachsorge-Bildgebung für die Beurteilung des Tumoransprechens innerhalb von 4 Monaten nach SIRT war nach 25 Behandlungen vorhanden. Eine höhere TNR korrelierte mit besserem Tumoransprechen, gemessen als posttherapeutische Reduktion von ETV%, in HCC (P=0.039), aber nicht in non-HCC (P=0.886). Schlussfolgerung: Diese Studie identifizierte ETV% als quantifizierbaren prätherapeutischen bildbasierten Biomarker für die 90Y Verteilung in der posttherapeutischen Bremsstrahlung SPECT in Patienten mit HCC und non-HCC. Zusätzlich war bei Patienten mit HCC, jedoch nicht bei Patienten mit non-HCC, eine höhere relative 90Y Aufnahme des Tumors mit besserem Tumoransprechen nach SIRT assoziiert.Purpose: To investigate baseline tumor imaging features that predict Yttrium-90 (90Y) distribution on posttreatment single photon emission computed tomography (SPECT) and tumor response to 90Y-transarterial radioembolization (TARE) in patients with primary and secondary liver tumors. Methods: This retrospective study included 38 patients with liver tumors who underwent resin-based TARE. The patient cohort consisted of 23 patients with hepatocellular carcinoma (HCC) and 15 patients with non-HCC hepatic malignancies. Multiphasic contrast-enhanced magnetic resonance imaging (MRI) or computed tomography (CT) scans were obtained prior to TARE, and Bremsstrahlung SPECT scans were captured immediately post-radioembolization. Total and enhancing tumor volume (ETV [cm3] and %), and total and enhancing tumor burden (%) were quantified on baseline MRI or CT. Up to two dominant tumors per treated liver lobe were included in the analysis. Non-rigid multimodal image registration of baseline scans and SPECT/CT was performed, and 90Y distribution was volumetrically assessed on posttreatment SPECT as tumor-to-normal-liver ratio (TNR). Tumor response was assessed according to established quantitative European Association for the Study of the Liver criteria (qEASL) and RECIST1.1 criteria. Clinical parameters, such as Child-Pugh class, were also assessed. Statistical analyses included non-parametric Mann-Whitney U test, bivariate Pearson correlation, and linear regression. Results: In HCC, higher ETV% on baseline imaging correlated with increased TNR on posttreatment SPECT, thus demonstrating higher 90Y-microsphere uptake in tumor relative to liver parenchyma (P<0.001). In non-HCC, higher baseline ETV% similarly correlated with increased TNR on SPECT (P=0.039). Moreover, HCC patients with Child-Pugh B showed more 90Y-microsphere deposition in nontumorous liver parenchyma, measured as lower TNR, compared to Child-Pugh A patients (P=0.021). Follow-up imaging within four months, and in turn response assessment, was available after 25 treatments. Higher TNR correlated with better tumor response, measured as a reduction of ETV% after treatment, in HCC (P=0.039), but not in non-HCC (P=0.886). Conclusion: In patients with HCC and non-HCC, ETV% may serve as a quantifiable baseline imaging biomarker to predict 90Y distribution on posttreatment Bremsstrahlung SPECT. Moreover, relatively higher tumor 90Y uptake was associated with better tumor response in patients with HCC, though this association was not evident in patients with non-HCC

Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review

Background: The widespread use of immune checkpoint inhibitors (ICIs) has revolutionised treatment of multiple cancer types. However, selecting patients who may benefit from ICI remains challenging. Artificial intelligence (AI) approaches allow exploitation of high-dimension oncological data in research and development of precision immuno-oncology. Materials and methods: We conducted a systematic literature review of peer-reviewed original articles studying the ICI efficacy prediction in cancer patients across five data modalities: genomics (including genomics, transcriptomics, and epigenomics), radiomics, digital pathology (pathomics), and real-world and multimodality data. Results: A total of 90 studies were included in this systematic review, with 80% published in 2021-2022. Among them, 37 studies included genomic, 20 radiomic, 8 pathomic, 20 real-world, and 5 multimodal data. Standard machine learning (ML) methods were used in 72% of studies, deep learning (DL) methods in 22%, and both in 6%. The most frequently studied cancer type was non-small-cell lung cancer (36%), followed by melanoma (16%), while 25% included pan-cancer studies. No prospective study design incorporated AI-based methodologies from the outset; rather, all implemented AI as a post hoc analysis. Novel biomarkers for ICI in radiomics and pathomics were identified using AI approaches, and molecular biomarkers have expanded past genomics into transcriptomics and epigenomics. Finally, complex algorithms and new types of AI-based markers, such as meta-biomarkers, are emerging by integrating multimodal/multi-omics data. Conclusion: AI-based methods have expanded the horizon for biomarker discovery, demonstrating the power of integrating multimodal data from existing datasets to discover new meta-biomarkers. While most of the included studies showed promise for AI-based prediction of benefit from immunotherapy, none provided high-level evidence for immediate practice change. A priori planned prospective trial designs are needed to cover all lifecycle steps of these software biomarkers, from development and validation to integration into clinical practice

Colorectal Cancer

Colorectal cancer is one of the commonest cancers affecting individuals across the world. An improvement in survival has been attributed to multidisciplinary management, better diagnostics, improved surgical options for the primary and metastatic disease and advances in adjuvant therapy. In this book, international experts share their experience and knowledge on these different aspects in the management of colorectal cancer. An in depth analysis of screening for colorectal cancer, detailed evaluation of diagnostic modalities in staging colorectal cancer, recent advances in adjuvant therapy and principles and trends in the surgical management of colorectal cancer is provided. This will certainly prove to be an interesting and informative read for any clinician involved in the management of patients with colorectal cancer

The Translational Status of Cancer Liquid Biopsies

Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. This can be achieved by leveraging omics information for accurate molecular characterization of tumors. Tumor tissue biopsies are currently the main source of information for molecular profiling. However, biopsies are invasive and limited in resolving spatiotemporal heterogeneity in tumor tissues. Alternative non-invasive liquid biopsies can exploit patient’s body fluids to access multiple layers of tumor-specific biological information (genomes, epigenomes, transcriptomes, proteomes, metabolomes, circulating tumor cells, and exosomes). Analysis and integration of these large and diverse datasets using statistical and machine learning approaches can yield important insights into tumor biology and lead to discovery of new diagnostic, predictive, and prognostic biomarkers. Translation of these new diagnostic tools into standard clinical practice could transform oncology, as demonstrated by a number of liquid biopsy assays already entering clinical use. In this review, we highlight successes and challenges facing the rapidly evolving field of cancer biomarker research. Lay Summary: Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. The discovery of biomarkers for precision oncology has been accelerated by high-throughput experimental and computational methods, which can inform fine-grained characterization of tumors for clinical decision-making. Moreover, advances in the liquid biopsy field allow non-invasive sampling of patient’s body fluids with the aim of analyzing circulating biomarkers, obviating the need for invasive tumor tissue biopsies. In this review, we highlight successes and challenges facing the rapidly evolving field of liquid biopsy cancer biomarker research